Syllabus for
Master of Technology (Computer Science and Engineering)
Academic Year  (2023)

The 2 year Post graduate program M.Tech in Computer Science and Engineering.started in 2011 . The course was started mainly to cater to the increasing demand for higher studies in the country. A growing intake with students from across the nation shows the popularity of the program. The Department strives to give skills essential to practicing engineering professionals; it is also an objective to provide experience in leadership, management, planning, and organization. The department understands its role in developing and evaluating methods that encourage students to continue to learn after leaving the university. We believe that the student opportunities and experiences should lead to an appreciation of the holistic development of individual. We also try to pass to our students our passion for what we do, and to have the students comprehend that we also desire to continue to learn.

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

PO2: Analyze complex engineering problems critically, apply independent judgment for synthesizing information to make intellectual and/or creative advances for conducting research in a wider theoretical, practical and policy context.

PO3: Think laterally and originally, conceptualize and solve engineering problems, evaluate a wide range of potential solutions for those problems and arrive at feasible, optimal solutions after considering public health and safety, cultural, societal and environmental factors in the core areas of expertise.

PO4: Develop and design real time projects more efficiently using math, statistics and analytics tools to deliver quality software solutions.

PO5: Analyze and apply the needs of computing in the society to promote novel and sustainable research ideas.

Assessment of each paper

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Course description:

The course is designed to equip the necessary awareness and command on the use of English language in writing a research paper starting from how to compile an appropriate title, language to use at different stages of a paper to make it effective and meaningful. 

Course objectives:

• Understand that how to improve your writing skills and level of readability
• Learn about what to write in each section.
• Understand the skills needed when writing a Title and ensure the good quality of paper at very first-time submission

          -       Useful phrases to ensure the quality of the paper

Adrian Wallwork, English for Writing Research Papers, Springer New York Dordrecht Heidelberg London, 2011

Highman N (1998), Handbook of Writing for the Mathematical Sciences, SIAM. Highman’sbook.

Course Description

Disaster Management (DM) is an emerging discipline which addresses all facets, namely, Mitigation, Preparedness, Response and Recovery. Global and national policies urge to consider its application in all branches of engineering, science, management and social sciences. The course would help the students to appreciate the importance of disaster science and its applications in reducing risks so as to contribute to national development. It would help the students to enhance critical thinking and to understand interdisciplinary approaches in solving complex problems of societies to reduce the risk of disasters.

Course Objectives

1.    To  demonstrate a critical understanding of key concepts in disaster risk reduction and humanitarian response 

2.    To critically evaluate disaster risk reduction and humanitarian response policy and practice from multiple perspectives. 

3.     To develop an understanding of standards of humanitarian response and practical relevance in specific types of disasters and conflict situations.

4.     To critically understand the strengths and weaknesses of disaster management approaches, planning and programming in different countries, particularly their home country or where they would be working 

Disaster: Definition, Factors And Significance; Difference Between Hazard And Disaster; Disaster and Hazard characteristics (Physical dimensions)

Repercussions of Disasters and Hazards: Economic Damage, Loss Of Human And Animal Life, Destruction Of Ecosystem. Disaster and Hazard typologies and their applications in Engineering. 

Study Of Seismic Zones; Areas Prone To Floods And Droughts, Landslides And Avalanches; Areas Prone To Cyclonic And Coastal Hazards With Special Reference To Tsunami; Post-Disaster Diseases And Epidemics

Preparedness: Monitoring Of Phenomena Triggering A Disaster Or Hazard; Evaluation Of Risk: Application Of Remote Sensing, Data From Meteorological And Other Agencies, Media Reports: Governmental And Community Preparedness. 

Concept And Elements, Disaster Risk Reduction, Global And National Disaster Risk Situation. Techniques Of Risk Assessment, Global Co-Operation In Risk Assessment And Warning, People’s Participation In Risk Assessment. Strategies for Survival.

Disaster Mitigation Meaning, Concept And Strategies Of Disaster Mitigation, Emerging Trends In Mitigation. Structural Mitigation And Non-Structural Mitigation, Programs Of Disaster Mitigation In India.

T1. Coppola, D, “Introduction to International Disaster Management “. Elsevier, 2015.

W1. http://www.training.fema.gov/emiweb/edu/ddemtextbook.asp

W2. https://www.weadapt.org/

W3. https://nagt.org/nagt/search_nagt.html?search_text=hazards&search=Go

W4. https://www.unisdr.org/

W5. https://emdat.be/

W6. http://bhuvan.nrsc.gov.in/bhuvan_links.php

W7. https://www.usgs.gov/

Course intends to highlight the value of education and self- development which would enable students to imbibe good values and understand the importance of character

Social values and individual attitudes, Work ethics, Indian vision of humanism, Moral and non- moral valuation. Standards and principles. Value judgements

Sense of duty. Devotion, Self-reliance. Confidence, Concentration, Truthfulness, Cleanliness, Honesty, Humanity. Power of faith, National Unity, Patriotism. Love for nature , Discipline

Soul and Scientific attitude, Positive Thinking. Integrity and discipline, Punctuality, Love and Kindness, Avoid fault Thinking, Free from anger, Dignity of labour, Universal brotherhood and religious tolerance, True friendship, Happiness Vs suffering, love for truth, Aware of self-destructive habits, Association and Cooperation, Doing best for saving nature, Character and Competence –Holy books vs Blind faith, Self-management and Good health, Science of reincarnation, Equality, Nonviolence ,Humility, Role of Women, all religions and same message, Mind your Mind, Self-control, Honesty, Studying effectively

Chakroborty, S.K. “Values and Ethics for organizations Theory and practice”, Oxford University Press, New Delhi, 1999

Chakraborty S K, "Ethics in Management: Vedantic Perspectives", Oxford University Press, New Delhi, India, 1997

Students will be able to:

 1. Understand the premises informing the twin themes of liberty and freedom from a civil rights perspective.

 2. To address the growth of Indian opinion regarding modern Indian intellectuals’ constitutional role and entitlement to civil and economic rights as well as the emergence of nationhood in the early years of Indian nationalism.

 3. To address the role of socialism in India after the commencement of the Bolshevik Revolution in 1917 and its impact on the initial drafting of the Indian Constitution.

History of Making of the Indian Constitution: History Drafting Committee, ( Composition & Working)

Philosophy of the Indian Constitution: Preamble Salient Features, ∙Contours of Constitutional Rights & Duties: ∙ Fundamental Rights ∙ Right to Equality ∙ Right to Freedom ∙ Right against Exploitation ∙ Right to Freedom of Religion ∙ Cultural and Educational Rights ∙ Right to Constitutional Remedies ∙ Directive Principles of State Policy ∙ Fundamental Duties.

Organs of Governance: ∙ Parliament ∙ Composition ∙ Qualifications and Disqualifications ∙ Powers and Functions ∙ Executive ∙ President ∙ Governor ∙ Council of Ministers ∙ Judiciary, Appointment and Transfer of Judges, Qualifications ∙ Powers and Functions

Local Administration: ∙ District’s Administration head: Role and Importance, ∙ Municipalities: Introduction, Mayor and role of Elected Representative, CEO of Municipal Corporation. ∙Pachayati raj: Introduction, PRI: ZilaPachayat. ∙ Elected officials and their roles, CEO Zila Panchayat: Position and role. ∙ Block level: Organizational Hierarchy (Different departments), ∙ Village level: Role of Elected and Appointed officials, ∙ Importance of grass root democracy

Election Commission: ∙ Election Commission: Role and Functioning. ∙ Chief Election Commissioner and Election Commissioners. ∙ State Election Commission: Role and Functioning. ∙ Institute and Bodies for the welfare of SC/ST/OBC and women.

SUBJECT OBJECTIVE:  

Students are encouraged to use various teaching aids such as over head projectors, power point presentation and demonstrative models. This will enable them to gain confidence in facing the placement interviews and intended to increase the score they earn on the upcoming exam above what they would otherwise earn. 

This course is specially designed for the students of higher degree. It aims to train and equip the students towards acquiring competence in teaching, laboratory skills, research methodologies and other professional activities including ethics in the respective academic disciplines. 

The course will broadly cover the following aspects:     Teaching skills     Laboratory skills and other professional activities     Research methodology For teaching suitable courses where strengthening in the training of the students is required will be identified and the student will be asked to prepare lectures on selected topics pertaining to the courses and present these lectures before a panel of faculty members. The student will also be required to prepare question papers which will test the concepts, analytical abilities and grasp in the subject. Wherever the laboratories are involved, students will also be asked to carry out laboratory experiments and learn about the use and applications of the instruments. The general guiding principle is that the students should be able to teach and participate in the undergraduate degree courses in his/her discipline in an effective manner. The students will also assist the faculty in teaching and research activities. The course will also contain the component of research methodology, in which a broad topic will be assigned to each student and he/ she is supposed to carry out intensive literature survey, data analysis and prepare a research proposal. 

Each group will carry out many professional activities beside teaching and research. Such as, purchase of equipments, hardware, software and planning for new experiments and also laboratories etc. Along with these the students will also be assigned some well defined activities. The student is expected to acquire knowledge of professional ethics in the discipline. 

Students are encouraged to use various teaching aids such as over head projectors, power point presentation and demonstrative models. This will enable them to gain confidence in facing the placement interviews and intended to increase the score they earn on the upcoming exam above what they would otherwise earn. 

This course is specially designed for the students of higher degree. It aims to train and equip the students towards acquiring competence in teaching, laboratory skills, research methodologies and other professional activities including ethics in the respective academic disciplines. 

The course will broadly cover the following aspects:     Teaching skills     Laboratory skills and other professional activities     Research methodology For teaching suitable courses where strengthening in the training of the students is required will be identified and the student will be asked to prepare lectures on selected topics pertaining to the courses and present these lectures before a panel of faculty members. The student will also be required to prepare question papers which will test the concepts, analytical abilities and grasp in the subject. Wherever the laboratories are involved, students will also be asked to carry out laboratory experiments and learn about the use and applications of the instruments. The general guiding principle is that the students should be able to teach and participate in the undergraduate degree courses in his/her discipline in an effective manner. The students will also assist the faculty in teaching and research activities. The course will also contain the component of research methodology, in which a broad topic will be assigned to each student and he/ she is supposed to carry out intensive literature survey, data analysis and prepare a research proposal. 

Each group will carry out many professional activities beside teaching and research. Such as, purchase of equipments, hardware, software and planning for new experiments and also laboratories etc. Along with these the students will also be assigned some well defined activities. The student is expected to acquire knowledge of professional ethics in the discipline. 

Each student will also prepare one presentation about his findings on the broad topic of research. The final report has to be submitted in the form of a complete research proposal. The References and the bibliography should be cited in a standard format. The research proposal should contain a) Topic of research b) Background and current status of the research work in the area as evident from the literature review c) Scope of the proposed work d) Methodology e) References and bibliography. 

A report covering laboratory experiments, developmental activities and code of professional conduct and ethics in discipline has to be submitted by individual student. 

The panel will jointly evaluate all the components of the course throughout the semester and the mid semester grade will be announced by the respective instructor to his student. 

A comprehensive viva/test will be conducted at the end of the semester jointly, wherever feasible by all the panels in a particular academic discipline/department, in which integration of knowledge attained through various courses will be tested and evaluated. 

To learn the systematic way of solving problems.

To understand the different methods of organizing large amounts of data.

To efficiently implement the different data structures.

To efficiently implement solutions for specific problems

Review of Analysis Techniques: Growth of Functions: Asymptotic notations; Standard notations and common functions; Recurrences and Solution of Recurrence equations- The substitution method, The recurrence – tree method, The master method; Amortized Analysis: Aggregate, Accounting and Potential Methods.

Graph Algorithms: Bellman - Ford Algorithm; Single source shortest paths in a DAG; Johnson’s Algorithm for sparse graphs; Flow networks and Ford -Fulkerson method; Maximum bipartite matching.

Polynomials and the FFT: Representation of polynomials; The DFT and FFT; Efficient implementation of FFT.

Number -Theoretic Algorithms: Elementary notions; GCD; Modular Arithmetic; Solving modular linear equations; The Chinese remainder theorem; Powers of an element; RSA cryptosystem; Primality testing; Integer factorization

String-Matching Algorithms: Naïve string Matching; Rabin - Karp algorithm; String matching with finite automata; Knuth-Morris-Pratt algorithm; Boyer – Moore algorithms.

Probabilistic and Randomized Algorithms: Probabilistic algorithms; Randomizing deterministic algorithms, Monte Carlo and Las Vegas algorithms; Probabilistic numeric algorithms.

Case Study: Comparison of Algorithm Design Strategies based on CPU, Memory, Disk and Network usages.

3.   Mark Allen Weiss, “Data Structures and Algorithm Analysis in Java”, 3rd edition,Pearson Education, 2012.

4. Aho, Hopcroft, Ullman, “Data Structures and Algorithms”, Pearson Education, 2009.

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 marks

CIA II  :   Mid Semester Examination (Theory)                                                : 25 marks

CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments    : 10 marks

     Attendance                                                                            : 05 marks

     Total                                                                                       : 50 marks

Data-driven decision making is becoming more common in organizations and businesses. In fact, database systems are at the center of the information systems strategies of most organizations. Users at any level of an organization can expect to work with and use database systems often. So, the ability to use these systems, which includes knowing what they can do and what they can't do, figuring out whether to access data directly or through technical experts, and knowing how to find and use the information well, became essential in every industry. Also, being able to design new systems and applications for them is a clear advantage and a necessity in the modern world. One type of database system that is widely used and the main focus of this course is the Relational Database Management System (RDBMS).

Database Management systems Application of DBMS, Advantages of DBMS-ER model, Components of E-R diagram, Cardinality – Relational databases, Converting ER Diagram into Relations/Tables

Introduction to Normalization using Functional and Multivalued Dependencies: Informal design guidelines for relation schema, Functional Dependencies, Normal Forms based on Primary Keys, Second and Third Normal Forms 

Simple queries in SQL, queries involving more than one relation, sub queries, full relational operations, Database modifications, defining a relational schema in SQL, view definitions.

Keys and foreign keys, constraints on attributes and tuples, modification of constraints, schema level constraints and Triggers.

Transaction Processing: Introduction to Transaction Processing, Transaction and System concepts, Desirable properties of Transactions, Characterizing schedules based on recoverability, Characterizing schedules based on Serializability, Transaction support in SQL.

Concurrency Control in Databases: Two-phase locking techniques for Concurrency control, Concurrency control based on Timestamp ordering, Multiversion Concurrency control techniques, Validation Concurrency control techniques, Granularity of Data items and Multiple Granularity Locking.

Recovery Concepts, NO-UNDO/REDO recovery based on Deferred update, Recovery techniques based on immediate update, Shadow paging, Database backup and recovery from catastrophic failures.

Introduction to OQL, Additional Forms of OQL Expressions, Object Assignment and Creation in OQL, User-Defined Types in SQL, Operations on Object-Relational Data.

Database Systems: The Complete Book by  Garcia-Molina, Jeffrey D. Ullman, Jennifer Widom, Pearson Education India; 2nd edition (1 January 2013)

50% ESE

The main goal of software development projects is to create a software system with a predetermined functionality and quality in a given time frame and with given costs. For achieving this goal. models are required for determining target values and for continuously controlling these values. This course focuses on principles, techniques, methods & tools for model-based management of software projects. Assurance of product quality and process adherence (quality assurance), as well as experience-based creation & improvement of models (process management). The goals of the course can be characterized as follows.

• Understanding the specific roles within a software organization as related to project and process management

• Understanding the basic infrastructure competencies (e.g., process modeling and measurement)

• Understanding the basic steps of project planning, and project management. Quality assurance, and process management and their relationships.

Conventional Software Management: The waterfall model, conventional software Management performance. Evolution of Software Economics: Software Economics. Pragmatic software cost estimation.

Improving Software Economics: Reducing Software product size, Improving software processes, improving team effectiveness. Improving automation, Achieving required quality, peer inspections. The old way and the new- The principles of conventional software engineering. Principles of modem software management, transitioning to an iterative process.

Life cycle phases: Engineering and production stages, inception. Elaboration, construction, transition phases. Artifacts of the process: The artifact sets. Management artifacts, Engineering artifacts, programmatic artifacts. Model based software architectures: A Management perspective and technical perspective.

Work Flows of the process: Software process workflow, Inter trans workflows. Checkpoints of the Process: Major Mile Stones, Minor Milestones, Periodic status assessments. Iterative Process Planning Work breakdown structures, planning guidelines, cost and scheduled estimating, Interaction, planning process, Pragmatic planning.

Project Control and Process instrumentation: The server care Metrics, Management indicators, and quality indicators. Life cycle expectations pragmatic Software Metrics, Metrics automation. Tailoring the Process: Process discriminates, Example. Future Software Project Management: Modem Project Profiles Next generation Software economics modem Process transitions.

Case Study: The Command Center Processing and Display System. Replacement (CCPDS. R).

1. Software Project Management. Walker Royce, Pearson Education 2010.

2. Software Project Management, Bob Hughes & Mike Cotterell, fourth edition, Tate McGraw HD 2012.

1. Applied Software Project Management, Andrew SteIbian 8 Jennifer Greene, O’Reilly. 2006

2. Head First PMP, Jennifer Greene & Andrew Steliman, ORoiHy.2007

3. Software Enneeñng Project Managent. Richard H. Thayer & Edward Yourdon, second edition, Wiley India, 2004.

4. Ale Project Management, Jim Highsniith. Pearson education, 2004

5. The art of Project management. Scott Berkun. O’Reilly, 2005.

6. Software Project Management in Practice. PankajJalote. Pearson Educabon,2002.

7. SEI.CMMI Tutorial, ww.sei.cmu.edu/cmmi/publications/stc.presentations/tutorial.html

End Semester Examination 50%.

 Data science is the study of data to get useful business information from it. It is a method for analyzing a lot of data that uses ideas and methods from math, statistics, artificial intelligence, and computer engineering. This analysis helps data scientists ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results. This course goes over the basics of data science and the algorithms for machine learning.   Using the programming language Python, the algorithm's implementation is discussed about. This course gives an overview of both the distributed environment and the deep learning techniques.

Data science process – roles, stages in data science project – working with data from files –relational and Non-Relational databases – exploring data – managing data – cleaning and sampling for modeling and validation – Data preprocessing-Statistics for Data Science-Data Distributions.

Choosing and evaluating models – mapping problems to machine learning, evaluating clustering models, validating models – cluster analysis – K-means algorithm unsupervised methods. , Naïve Bayes – Memorization Methods – Linear and logistic regression – unsupervised methods. 

Data Analysis with Numpy  and Pandas – Visualization with Seaborn Matplotlib, Plotly and Cufflinks – Scikit -learn –Regression, KNN, PCA and SVM in Python– Recommender systems – NLP with NLTK – Neural Nets and Deep Learning with Tensor Flow

Introduction –Hadoop vs Spark - Spark Data Frame – Group by and Aggregate –RDD(Resilient Distributed Datasets) – Spark SQL – Spark Running on Cluster–Machine Learning with Mlib–Collaborative Filtering–NLP Applications–Spark Streaming.

CNN Architectures – Convolution – Pooling Layers – Transfer Learning – Image Classification using Transfer Learning – Recurrent and Recursive Nets – Recurrent Neural Networks – Deep Recurrent Networks – Recursive Neural Networks – Applications.

T2: William McKinney- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Ipython, O'Reilly; Second edition, 2017.

T3 : Sandy Ryza, Uri Laserson.   Advanced Analytics with Spark: Patterns for Learning from Data at Scale  – O'Reilly 2017.

T4: Ian J. Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press, 2017.

R2: Francois Chollet, “Deep Learning with Python”, Manning Publications, 2018.

ESE - 50 Marks

● To increase the knowledge of advanced paradigms of algorithm design.
● To make the students learn an object oriented way of solving problems.
● To Enhance students’ capability of selecting the best and efficient way for encoding problems.

1. Implementation of Sorting Techniques like Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort and Heap Sort and Compare their performances.
2. Implementation of Linear Search and Binary Search Algorithms and Compare their performances.
3. Mini Project Details: Selection of Topic and Research about the Selected Topic.

1. Implementation of Shortest Path Algorithm (Bellman Ford).
2. Implementation of Single Source Shortest Path in a DAG using Dijkstra's Algorithm.
3. Mini Project Details: Choosing the right Dataset for the chosen Title/Topic and Exploratory Data Analysis (EDA).

1. Implement Chinese Remainder Theorem Algorithm.
2. Implement RSA Public-Key Cryptosystem.
3. Mini Project Details: Implementation (2 Phases) - Phase 1

1. Design, develop, and write a program to solve String Matching Problem using naïve approach, the KMP and Robin Karp algorithm. Compare their performances.
2. Mini Project Details: Implementation (2 Phases) - Phase 2

1. Design, develop, and write a program to implement a Monte Carlo algorithm to test the primality of a given integer and determine its performance.
2. Implement any Las Vegas Algorithm used for Randomization.
3. Mini Project Details: Performance Analysis and Report Submission

Records: 05 marks

Mid semester examination: 10 marks

Class work: 10 marks

Total: 50 marks

Course will give students a chance to use what they learn in the lectures, homework, SQL assignments, and a database implementation project.

ESE 50 %

Total Marks= 50

The aim of the course is to introduce the research methodology, the understanding on the research, methods, designs, data collection methods, report writing styles and various dos and don’ts in research.

Meaning, Objectives and Characteristics of research - Research methods Vs Methodology, Different Research Design: Types of research - Descriptive Vs. Analytical, Applied Vs. Fundamental, Quantitative Vs. Qualitative, Conceptual Vs. Empirical, Research process - Criteria of good research - Developing a research plan. 

Defining the research problem - Selecting the problem - Necessity of defining the problem - Techniques involved in defining the problem - Importance of literature review in defining a problem - Survey of literature - Primary and secondary sources - Reviews, treatise, monographs, thesis reports, patents - web as a source - searching the web - Identifying gap areas from literature review - Development of working hypothesis. 

Selection of Appropriate Data Collection Method: Collection of Primary Data, Observation Method, Interview Method, Email, Collection of Data through Questionnaires, Collection of Data through Schedules, Collection of Secondary Data – internal & external. Sampling process: Direct & Indirect Methods, Non-probability sampling, Probability sampling: simple random sampling, systematic sampling, stratified sampling, cluster sampling, Determination of sample size; Analysis of data using different software tools. 

Processing Operations, Types of Analysis, Statistics in Research, Measures of: Central Tendency, Dispersion, Asymmetry and Relationship, correlation and regression, Testing of Hypotheses for single sampling: Parametric (t, z and F), Chi Square, Logistic regression, ANOVA, non-parametric tests. Numerical problems.

IPR: Invention and Creativity- Intellectual Property-Importance and Protection of Intellectual Property Rights (IPRs)- A brief summary of: Patents, Copyrights, Trademarks, Industrial Designs; Publication ethics, Plagiarism check.

Research Writing: Interpretation and report writing, Techniques of interpretation, Types of report – letters, articles, magazines, transactions, journals, conferences, technical reports, monographs and thesis; Structure and components of scientific writing: Paragraph writing, research proposal writing, reference writing, summarizing and paraphrasing, essay writing; Different steps in the preparation - Layout, structure and language of the report – Illustrations, figures, equations and tables.

T2. Garg, B.L., Karadia, R., Agarwal, F. and Agarwal, “An introduction to Research Methodology”, RBSA Publishers, 2002.

T3. Day, R.A., “How to Write and Publish a Scientific Paper”, Cambridge University Press, 1992.

R2. Sarah J Tracy, “Qualitative Research Methods” Wiley Balckwell- John wiley & sons, 1/e, 2013.

R3. James Hartley, “Academic Writing and Publishing”, Routledge Pub., 2008.

End Semester Examination - 50%

Review existing evidence on the review topic to inform programme design and policy making undertaken by the DfID, other agencies and researchers. Identify critical evidence gaps to guide the development.

Aims and rationale, Policy background, Conceptual framework and terminology ∙ Theories of learning, Curriculum, Teacher education. ∙ Conceptual framework, Research questions. ∙ Overview of methodology and Searching.

 Pedagogical practices are being used by teachers in formal and informal classrooms in developing countries.  Curriculum, Teacher education.

 Methodology for the in-depth stage: quality assessment of included studies. ∙ How can teacher education (curriculum and practicum) and the school curriculum and guidance materials best support effective pedagogy? ∙ Theory of change. ∙ Strength and nature of the body of evidence for effective pedagogical practices. ∙ Pedagogic theory and pedagogical approaches. ∙ Teachers’ attitudes and beliefsand Pedagogic strategies.

Alignment with classroom practices and follow-up support ∙ Peer support ∙Support from the head teacher and the community. ∙ Curriculum and assessment ∙ Barriers to learning: limited resources and large class sizes.

Research design ∙ Contexts ∙ Pedagogy ∙ Teacher education ∙ Curriculum and assessment ∙ Dissemination and research impact.

ii.              Akyeampong K (2003) Teacher training in Ghana - does it count? Multi-site teacher education research project (MUSTER) country report 1. London: DFID.

iii.            Akyeampong K, Lussier K, Pryor J, Westbrook J (2013) Improving teaching and learning of basic maths and reading in Africa: Does teacher preparation count? International Journal Educational Development, 33 (3): 272–282.

iv.             Alexander RJ (2001) Culture and pedagogy: International comparisons in primary education. Oxford and Boston: Blackwell.

v.               Chavan M (2003) Read India: A mass scale, rapid, ‘learning to read’ campaign.

www.pratham.org/images/resource%20working%20paper%202.pdf.

Duringtheseminarsessioneachstudentisexpectedtoprepare and presentatopicon engineering/ technology, itis designed to:

• Review and increasetheir understandingof thespecific topics tested.
• Improvetheir abilityto communicate that understandingto thegrader.
• Increasetheeffectiveness with which theyusethelimited examinationtime.

 students towards acquiring competence in teaching, laboratoryskills, research methodologies and otherprofessional activities includingethics in the respective academicdisciplines.

The course will broadly cover the following aspects:

• Teachingskills
• Laboratoryskills andother professional activities
• Research methodology

Notices pertaining to this course will be displayed on the respective departmental notice boards by the panel coordinator/instructor.Students may also check the exam notice board for notices issued by the exam division.

MAKEUPPOLICY:  All students are required to attend all the lectures and presentations in the panel. Participation and cooperation will also be taken into account in the final evaluation. Requests for makeup should normally be avoided. However,in genuine cases,panel will decide action on a case by case basis.

NOTE:Seminar shall be presented in the department in presence of a committee (Batch of Teachers)constituted by HOD.The seminar marks are to be awarded by the committee. Students shall submit the seminar report in the prescribed Standard format.

This course covers the major aspects of computer and network security. It starts with a general introduction to information security, and then proceeds to cover types of threats and attacks, hacking techniques, network vulnerabilities, security policies and standards, firewalls, cryptography, Authentication & digital signatures, the SSL protocol, Wireless security, intrusion detection and prevention. 

Public key cryptography principles, public key cryptography algorithms, digital signatures, digital Certificates, Certificate Authority and key management, Kerberos, X.509 Directory Authentication Service.

Email privacy: Pretty Good Privacy (PGP) and S/MIME. IP Security Overview, IP Security Architecture, Authentication Header, Encapsulating Security Payload, Combining Security Associations and Key Management.

Web Security Requirements, Secure Socket Layer (SSL) and Transport Layer Security (TLS), Secure Electronic Transaction (SET). Intruders,

Viruses and related threats. Firewall Design principles, Trusted Systems. Intrusion Detection Systems.

2. Principles of Information Security, Whitman, Thomson. 2010

3. Network Security:The complete reference,Robert Bragg,Mark Rhodes, TMH 2010

4. Introduction to Cryptography, Buchmann, Springer. 2012

Education, 5th  Edition 2013.

ESE Marks:50

Course Description:

This course will cover fundamental concepts used in Data and Web Analytics. Models and Algorithms for Intelligent Data Analysis to solve several real-life problems will be covered to have hands-on practice. Data  analytics allows to find relevant information, structures, and patterns, to gain new insights, to identify causes and effects, to predict future developments, or to suggest optimal decisions. We need models and algorithms to collect, preprocess, analyze, and evaluate data. This involves methods from various fields such as statistics, machine learning, pattern recognition, systems theory, operations research, or artificial intelligence. The course aims to learn the most important methods and algorithms for data analytics. This course helps to select appropriate methods for specific tasks and apply these in one’s  own data analytics projects. 

Course Objective:

·       To understand the need for Data Analytics and Web Analytics

·       To discover various paradigm of Models and Algorithms for Intelligent Data Analysis.

·       To design suitable Data Analytics Algorithms for solving problems

·       To summarize the fundamentals of Web Analytics

·       To demonstrate the various data preprocessing and web analytics data collection techniques

·       To make use of data and web analytics techniques to solve real life problems

                      ·       To experiment with various applications of data analytics and web analytics

Introduction, It’s All About Data,  Data Analytics, Data Mining, and Knowledge Discovery, Data and Relations, The Iris Data Set, Data Scales, Set and Matrix Representations, Relations, Dissimilarity Measures, Similarity Measures, Sequence Relations,  Sampling, and Quantization. Differences between Data Analytics and Web Analytics, Case Study – Web Analytics, Current Landscape and Challenges,  Web Analytics Fundamentals,  Capturing Data,  Selecting  Optimal Web Analytics Tool, Understanding Clickstream Data Quality,  Implementing Best Practices, Apply the “Three Layers of So What” Test.

Data Preprocessing-Error Types, Error Handling,  Filtering Data Transformation, Data Integration, Problems, Data Visualization  Diagrams, Principal Component Analysis,  Multidimensional Scaling, Sammon Mapping, Auto-encoder, Histograms, Spectral Analysis,  Case Study web analytics, Data Collection—Importance and Options,Understanding the Data Landscape, Clickstream Data, Outcomes Data, Research Data, Competitive Data.

Correlation, Linear Correlation,  Correlation and Causality, Chi-Square Test for Independence, Problems, Regression, Linear Regression, Linear Regression with Nonlinear Substitution, Robust Regression, Neural Networks, Radial Basis Function Networks, Cross-Validation, Feature Selection, Problems .

Forecasting, Finite State Machines,  Recurrent Models, Autoregressive Models, Problems and Use cases, Classification, Classification Criteria, Naive Bayes Classifier, Linear Discriminant Analysis,  Support Vector Machine,  Nearest Neighbor Classifier,  Learning Vector Quantization,  Decision Trees, Problems.

Clustering, Cluster Partitions,  Sequential Clustering, Prototype-Based Clustering, Fuzzy Clustering, Relational Clustering, Cluster Tendency Assessment,  Cluster Validity,  Self-organizing Map, Problems and Use cases, Case study related to Web Analytics perspective of Creating a Data-Driven Culture—Practical Steps and Best Practices,  Key Skills to Look for in a Web Analytics Manager/Leader.

2. Kaushik, A. (2007). Web analytics: An hour a day (W/Cd). John Wiley & Sons.

Course Description:

The course will help the students understand the fundamental digital image processing concepts. The students will also gain knowledge of image compression techniques followed by image segmentation. The course will also help the students to use Deep Learning techniques for feature extraction and image pattern classification.

Course Objective:

1. The students will learn the fundamental concepts of Image Processing.

2. The students will learn image compression and segmentation techniques.

3. The students will study feature extraction and pattern classification techniques.

Steps in digital image processing-Elements of visual perception- brightness adaptation, Mach band effect. Image enhancement in spatial and frequency domain, Histogram equalization

Edge detection, Thresholding, Region growing, Fuzzy clustering, Watershed algorithm, Active contour models, Texture feature based segmentation, Graph based segmentation, Wavelet based Segmentation - Applications of image segmentation.

First and second order edge detection operators, Phase congruency, Localized feature extraction -detecting image curvature, shape features, Hough transform, shape skeletonization, Boundary descriptors, Moments, Texture descriptors- Autocorrelation, Co-occurrence features, Runlength features, Fractal model based features, Gabor filter, wavelet features.

Registration - Preprocessing, Feature selection - points, lines, regions and templates Feature correspondence - Point pattern matching, Line matching, Region matching, Template matching. Transformation functions - Similarity transformation and Affine Transformation. Resampling – Nearest Neighbour and Cubic Splines. 

Sources of 3D Data sets, Slicing the Data set, Arbitrary section planes, The use of color, Volumetric display, Stereo Viewing, Ray tracing, Reflection, Surfaces, Multiple connected surfaces.

2. Mark Nixon, Alberto Aguado, “Feature Extraction and Image Processing”, Academic Press, 2008.

2.John C.Russ, “The Image Processing Handbook”, CRC Press,2007.

3.Mark Nixon, Alberto Aguado, “Feature Extraction and Image Processing”, Academic Press, 2008.

4.Ardeshir Goshtasby, “ 2D and 3D Image registration for Medical, Remote Sensing and Industrial Applications”, John Wiley and Sons,2005.

ESE - 30

Course Description:

This course will teach you about the characteristics of Big Data and how to use it in Big Data Analytics. You will learn about the features, benefits, limitations, and applications of various Big Data processing tools. You'll learn how Hadoop, Hive, Apache Spark can help you reap the benefits of Big Data while overcoming some of its challenges. At the end of completing this course students will get job opportunities in the field of data engineering.  

Course Objective:

•To optimize business decisions and create competitive advantage with Big Data analytics.

•To explore the fundamental concepts of big data analytics. 

•To learn to analyze the big data using intelligent techniques. 

•To understand and analyze the applications using Map Reduce Concepts. 

•To introduce programming tools PIG & HIVE in Hadoop echo system.

What is big data – why big data –Data,  Evolution of Big data; Characteristics of Big Data; Types of data; Sources of data; Data Storage and Analysis, Comparison with Other Systems, Rational Database Management System , Grid Computing, Volunteer Computing, Types of analytics; Domain Specific Examples of Big Data; Analytics Flow for Big Data;

Experiment 1:  Illustrate a python program to read and analyze the big data using EDA. 

Getting Started with NoSQL and MongoDB: Introducing NoSQL and MongoDB, Installing and Configuring MongoDB;  Implementing NoSQL in MongoDB: Configuring User Accounts and Access Control Managing Databases and Collections from the MongoDB Shell;  Finding Documents in the MongoDB Collection from the MongoDB Shell; Additional Data-Finding Operations Using the MongoDB Shell; Manipulating MongoDB Documents in a Collection; Utilizing the Power of Grouping, Aggregation, and Map Reduce; Implementing MongoDB in Python Applications;

Experiment 2:  

i.Construct databases, collections and perform CRUD operations in MongoDB shell.

ii.Apply MongoDB in Python Applications;

History of Hadoop; HDFC concepts; Design of HDFS; Hadoop Distributed File System and its Features; Components of Hadoop; HDFS Commands;  Analyzing the Data with Hadoop;  Scaling Out ; Hadoop Streaming; Reading and writing data in Hadoop; Directories; Querying the File system; Deleting data; Data flow: Anatomy of a File Read and File Write;

Experiment 3: 

i.Experiment with various Hadoop commands in Hadoop environment.

ii.Develop python/java programs Reading and writing data in Hadoop.

Anatomy of a YARN Application Run – Resource Requests, Application Lifespan, Building YARN Applications; Scheduling in YARN; YARN Distributed-Shell Structure of YARN Applications; 

Experiment 4: Build YARN Applications

The Configuration API; Setting Up the Development Environment; Writing a Unit Test with MRunit – mapper and reducer; Running Locally on Test Data; Running on a cluster; MapReduce workflows ; Anatomy of MapReduce job run; Failures; shuffle and sort; MapReduce types – input formats – output formats;

Experiment 5: Analyze various map-reduce java/python applications in Hadoop platform.

2. Brad Dayley  Sams Teach Yourself NoSQL with MongoDB in 24 Hours.

3. Douglas Eadline, “Hadoop 2 Quick-Start Guide: Learn the Essentials of Big Data Computing in the Apache Hadoop 2 Ecosystem”, 1st Edition, Pearson Education, 2016.

4. Tom White, “Hadoop: The Definitive Guide”, 4th Edition, O’Reilly Media, 2015

2. Ankam, Venkat. Big Data Analytics. India: Packt Publishing, 2016.

End Semester Examination(ESE)                 : 50% (50 marks out of 100 marks)

IoT architecture is the system of multiple heterogeneous elements that range from sensors, protocols, actuators and controllers to AI and cloud services. This course will equip the students to build futuristic IoT solutions utilizing all these elements adhering to global standards. Various computing models for IoT implementations are also introduced with case studies. The students will develop skills in identifying the requirements for the target IoT systems, selecting the appropriate hardware and software platforms and implementing and deploying the solutions.

Introduction, Architectures, Applications, Devices, Security and privacy

Introduction to generic IoT architecture, Protocols, Standards, Databases, Time Bases, IoT Devices

The IoT Architectural Reference Model as Enabler, Guidance to the ARM, IoT Reference Model, IoT Reference Architecture, The IoT ARM Reference Manual, Toward a Concrete Architecture

Standards, Interoperability, Discoverability, Security and Privacy in IoT

Cloud computing, Big data, Big-stream-oriented Architecture, Performance Evaluation, Fog Computing and the IoT, Virtualization and Replication, The IoT Hub, Operational Scenarios, Synchronization Protocol.

Hardware for the IoT , Classes of Constrained Devices, Hardware Platforms, Software for the IoT, Networking, Programming Model, Integration Challenges, Implementation and Validation.

Cirani, Simone, et al. Internet of things: architectures, protocols and standards. John Wiley & Sons, 2018.

ESE 50 Marks

This course caters to the hand-on experience of Network security concepts such as encryption techniques  and hashing using security tools.

Experiment 1:  Implement the following algorithms a) DES b) RSA Algorithm .

Experiment 2:Implement the following algorithms  Diffiee-Hellman , MD5 , SHA-1

Experiment 3: Fire wall implementation using different security requirements.

Experiment 4: Demonstrate intrusion detection system (ids) using any tool (snort or any other s/w)

Experiment 5:  Implement somesimple filtering rules based on IP and TCP header information

ESE MARKS:50

Course Description:

This course will cover hands on implementation of the fundamental concepts used in Data and Web Analytics. Models and Algorithms for Intelligent Data Analysis to solve several real-life problems will be covered to have hands-on practice. 

Course Objective:

•  To demonstrate the various data preprocessing and web analytics data collection techniques
• To make use of data and web analytics techniques to solve real life problems
• To experiment with various applications of data analytics and web analytics

2. Kaushik, A. (2007). Web analytics: An hour a day (W/Cd). John Wiley & Sons.

This course provides a strong foundation of fundamental concepts in Artificial Intelligence. To provide an empirical evidence and the scientific approachapplyingArtificial Intelligence techniques for problem solving using probabilistic, fuzzy, statistical and Deep Learning Models.

Games, Optimal Decisions in Games,The minimax algorithm,Optimal decisions in multiplayer games, Alpha Beta Pruning, Move ordering, Imperfect Real-Time Decisions,Cutting off search, Forward pruning.Stochastic Games,Evaluation functions for games of chance, Partially Observable Games, Krieg spiel: Partially observable chess,Card games, State-of-the-Art Game Programs, Alternative Approaches 

Convolutional Neural Networks, Motivation, Convolution operations, Pooling,  Image classification, Modern CNN architectures, Recurrent Neural Network, Motivation, Vanishing/Exploding gradient problem, Applications to sequences, Modern RNN architectures, Tuning/Debugging Neural Networks, Parameter search, Overfitting, Visualizations, Pretrained Models.

Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out of 100 marks)

 End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I  :  Quizzes/Seminar/Case Studies/Project Work /Assignments     : 10 marks

CIA II  :   Mid Semester Examination (Theory)                                   : 25 marks

CIA III  : Quizzes/Seminar/Case Studies/Project Work /Assignments    : 10 marks

     Attendance                                                                            : 05 marks

           Total                                                                                       : 50 marks

Internships are short-term work experiences that will allow  a student to observe and participate in professional work environments and explore how his interests relate to possible careers. They are important learning opportunities trough industry exposure and practices.   More specifically, doing internships is beneficial because they provide the opportunity to:

▪        Get an inside view of an industry and organization/company

▪        Gain valuable skills and knowledge

▪        Make professional connections and enhance student's network

▪        Get experience in a field to allow the student  to make a career transition

1.The student shall undergo an Internship for 30 days  starting from the end of 2nd semester examination and completing it during the initial period of 3rd semester.

2.The department shall nominate a faculty as a mentor for a group of students to prepare and monitor the progress of  the students

3. The students shall report the progress of the internship to the mentor/guide at regular intervals and may seek his/her advise.

4. The Internship shall be completed by the end of  7th semesters.

5. The students are permitted to carry out the internship outside India with the following conditions, the entire expenses are to be borne by the student and the University will not give any financial assistance.

6. Students can also undergo internships arranged by the department during vacation.

7. After completion of Internship, students shall submit a report to the department with the approval of both internal and external guides/mentors.

8. There will be an assessment for the internship for 2 credits, in the form of report assessment by the guide/mentor  and a presentation on the internship given to department constituted panel.

During this project phase I session, each student is expected to prepare and present a topic on engineering/ technology on their domain interest to persue the project work, it is designed to: 

• Review and increase their understanding of the specific topics identified.
  
• Improve their ability to communicate that understanding to the grader.
• Increase the effectiveness with which they use the limited examination time.

Assessment of Project Work(Phase I) ▪ Continuous Internal Assessment:100 Marks ♦ Presentation assessed by Panel Members ♦ Guide ♦ Mid-semester Project Report

▪          Continuous Internal Assessment:100 Marks

♦        Presentation assessed by Panel Members

♦        Guide

♦        Mid semester Project Report

End semester Examination :100 Marks

Presentation assessed by Panel Members

♦        Guide

♦        End semester Project Report

This course aims at making the students get an understanding of the compression techniques available for multimedia applications and also get an understanding of the encryption that can be implemented along with the compression.

Compaction techniques – Huffmann coding – Adaptive Huffmann Coding – Arithmatic coding – Shannon-Fano coding – Dictionary techniques – LZW family algorithms

Video compression techniques and standards – MPEG Video Coding I: MPEG – 1 and 2 – MPEG Video Coding II: MPEG – 4 and 7 – Motion estimation and compensation techniques – H.261 Standard

Introduction: Services, Mechanisms and Attacks, OSI security Architecture, Model for network Security; Classical Encryption Techniques:Symmetric Cipher Model, Substitution Techniques, Transposition Techniques, Rotor Machines, Stegnography;

Block Ciphers and Data Encryption Standard: Simplified DES, Block Cipher Principles, Data Encryption Standard, Strength of DES, Differential and Linear Crypt Analysis, Block Cipher Design Principles, Block Cipher Modes of Operation

2. David Salomon : Data Compression – The Complete Reference, Springer Verlag New York Inc., 4th Edition, 2006

3. Yun Q.Shi, HuifangSun : Image and Video Compression for Multimedia Engineering -  Fundamentals, Algorithms & Standards, CRC press, 2008

4.Jan Vozer : Video Compression for Multimedia, AP Profes, NewYork, 1995.

5. William Stallings, “Cryptography and Network Security”, 6th. Ed, Prentice Hall of India, New Delhi ,2013

6. William Stallings, “Network Security Essentials”, 5^thed. Prentice Hall of India, New Delhi

ESE-50

During this project phase I session, each student is expected to prepare and present a topic on engineering/ technology on their domain interest to persue the project work, it is designed to: 

• Review and increase their understanding of the specific topics identified.
  
• Improve their ability to communicate that understanding to the grader.
• Increase the effectiveness with which they use the limited examination time.

Assessment of Project Work(Phase II) and Dissertation ▪ Continuous Internal Assessment:100 Marks ♦ Presentation assessed by Panel Members ♦ Assessed by Guide ♦ Mid Semester Project Report ▪ End Semester Examination:100 Marks ♦ Viva Voce ♦ Demonstration ♦ Project Report

▪ Continuous Internal Assessment:100 Marks

♦ Presentation assessed by Panel Members

♦ Assessed by Guide

♦ Mid Semester Project Report

▪ End Semester Examination:100 Marks

♦ Viva Voce

♦ Demonstration

♦ Project Report

▪ Dissertation (Exclusive assessment of Project Report): 100 Marks

♦ Internal Review : 50 Marks

♦ External review : 50 Marks